Compressed sensing approach for pattern synthesis of maximally sparse non‐uniform linear array

Compressed sensing (CS) has been successfully applied to the synthesis of maximally sparse non‐uniform linear array with the synthesised pattern matching the reference pattern very well by using as few elements as possible. According to the CS theory, a sparse or compressible high‐dimensional signal can be first projected onto a low‐dimensional space through a measurement matrix, and then recovered accurately by using a variety of practical algorithms based on the low‐dimensional information. The proposed approach can synthesise the sparse linear arrays fitting the desired patterns with a minimum number of elements. Numerical simulations validate the effectiveness and advantages of the proposed synthesis method. Moreover, compared with the existing sparse‐array synthesis methods, the author's method is more robust and accurate, while maintaining the advantage of easy implementation.